Explain the architecture of large-scale LLMs?
QQuestion
Can you explain the architecture of large-scale language models?
AAnswer
A typical LLM architecture includes:
Transformer Networks: At the core of most contemporary LLMs lies the Transformer architecture. This neural network departs from traditional recurrent neural networks (RNNs) and excels at understanding long-range dependencies within sequences, making it particularly well-suited for language processing tasks. Transformers consist of two sub-components:
Encoder: This section processes the input text, breaking it into a series of encoded representations, capturing the relationships between words.
Decoder: Here, the model leverages the encoded information from the encoder to generate the output text, one word at a time.
Self-Attention: This ingenious mechanism within the Transformer allows the model to focus on the most relevant parts of the input sequence for a given word or phrase. It attends to different parts of the input text differentially, depending on their importance to the prediction at hand. This capability is crucial for LLMs to grasp the nuances of language and context.
Input Embeddings and Output Decoding
Input Embedding: Word embedding transforms text data into numerical representations Before feeding text data into the LLM. This process converts words into vectors, capturing their semantic similarities and relationships.
Output Decoding: Once the LLM has processed the encoded input, it translates the internal representation back into human-readable text through decoding
Model Size and Parameter Count: The number of parameters (weights and biases) within an LLM significantly impacts its capabilities. Large-scale LLMs often have billions, or even trillions, of parameters, allowing them to learn complex patterns and relationships within language data. However, this also necessitates substantial computational resources for training and running the model.
EExplanation
Architecture of Large Language Models (LLMs) is described as below:
Input Layer:
Tokenization: The input text is broken down into smaller units called tokens, which can be words, subwords, or characters. These tokens are then converted into numerical representations (embeddings) that the model can process.
Embedding Layer: Includes Word Embeddings, Positional Embedding. In Word Embeddings, each token is mapped to a dense vector in a high-dimensional space, representing its semantic meaning. In Positional Embeddings, since transformers do not inherently understand the order of tokens, positional embeddings are added to the word embeddings to give the model information about the token positions within a sentence.
Transformer Architecture:
Self-Attention Mechanism
Attention Scores: The self-attention mechanism computes a set of attention scores that determine how much focus each word should give to other words in the sequence.
Query, Key, and Value (Q, K, V): These are linear projections of the input embeddings used to compute attention. The model calculates the relevance of each token to others using the dot product of Query and Key vectors, followed by a softmax operation to obtain attention weights. The Value vectors are then weighted by these attention scores.
Multi-Head Attention: Multiple attention heads are used to capture different aspects of the relationships between tokens. Each head operates in a separate subspace, and the results are concatenated and projected back into the original space.
Feedforward Neural Network: After the attention mechanism, the output is passed through a feedforward neural network (a series of dense layers with activation functions), applied independently to each position.
Layer Normalization and Residual Connections: Each sub-layer (attention and feedforward) is followed by layer normalization and a residual connection, which helps stabilize training and allows for deeper networks. Stacking Layers
Transformer Blocks: The architecture typically involves stacking multiple transformer layers (or blocks) on top of each other. Each block consists of a multi-head self-attention mechanism and a feedforward neural network. This stacking allows the model to learn complex hierarchical representations of the data.
Output Layer: Decoding
Language Modeling Objective: In autoregressive models like GPT, the model is trained to predict the next token in a sequence given the previous tokens. In masked language models like BERT, the model predicts missing tokens in a sequence.
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